NotebookLM: The AI Tool Helping Librarians Cut Through Information Overload

If you’re someone who lives in a world of research, information overload is likely! From articles and documents to podcasts and interviews, resources pile up but they can’t talk to each other. Connecting the dots is left to you.

Enter NotebookLM, a game-changing AI tool quietly reshaping how we interact with information. Built by Google Labs, it combines the power of a personalised research assistant with the simplicity of a note-taking app. It offers something entirely new: a “grounded” chatbot that only uses the sources you upload.

In this post, I’ll take you through what makes NotebookLM different, why it matters for educators and librarians, and how we can use it effectively. You’ll come away with a working understanding of the tool, creative use cases, and resources to dive deeper. Let’s get started.

What is NotebookLM?

NotebookLM is a “source-grounded” language model. That means it doesn’t answer based on its training data or scrape the open internet. Instead, you upload your own varied sources – examples above – and ask questions, generate summaries, or build study guides based solely on that content.

It’s part notebook, part learning assistant, and part AI-powered explainer, designed to help you synthesise complex information, not just retrieve it. This makes it fundamentally different from ChatGPT, which excels at general responses but often “hallucinates” or misattributes content.

Why librarians and educators should care about NotebookLM

NotebookLM answers a specific pain point in education and research: contextual relevance.

In traditional search, users look for answers across billions of webpages. In NotebookLM, you choose what is in your source library. This dramatically shifts the role of AI from answer-generator to thinking partner. And that shift is crucial.

Here’s why:

Librarians can model good information behaviour – curating trusted sources, questioning assumptions, and demonstrating digital literacy in action.

Students can build study guides or syntheses from class notes and readings – no more flipping between tabs and apps.

Professionals in law, health, or academia can surface insights from internal documentation or white papers quickly and transparently.

In every case, the user is in control of the sources, and therefore, the knowledge which is collated.

What can you do with NotebookLM – and what you can’t?

Functionality

This tool is still in beta, but the functionality is already impressive. You can:

Upload and combine multiple sources into a notebook
Highlight, annotate, and ask questions of your materials
Auto-generate FAQs, study guides, mind maps, and summaries
Create audio or video overviews of your sources
Extract citations, quote evidence, and check for inconsistencies.

Advantages

If NotebookLM can’t answer your question, it will tell you, usually because the information simply isn’t in your uploaded materials. That’s not a flaw. It’s a feature. In a time when AI tools often sound more confident than correct, this groundedness is refreshing. This invites the user to:

Re-evaluate their assumptions
Upload better sources
Accept ambiguity

In other words, NotebookLM helps teach the discipline of inquiry. And that might be the most valuable lesson of all.

Opportunities

You can navigate the tool efficiently and easily. The interface is clean, simple, and doesn’t require any technical skills. That said, users may still need help structuring notebooks or crafting effective prompts. This is a great opportunity for librarians and educators to take the lead:

Share example notebooks to model good practice
Embed it into research skills or information literacy sessions
Use it to demonstrate note-taking, summarisation, and evidence extraction techniques

Pro tip: Start with 3–5 well-structured sources. A focused notebook gives better results than an unfocused one with 50 documents.

Use Case: Writing a conference paper for BIALL

At the conference this year, I shared with a colleague how I’d pulled together all my materials into one NotebookLM workspace. Being able to ask questions across the full collection meant I could easily spot repeated themes, compare different viewpoints, and even track down a quote I’d half-remembered from a panel session.

It saved me hours of time. More importantly, it gave me confidence that the insights I was using came directly from trusted sources, not hallucinated or misremembered. I especially appreciated being able to generate my notes as a podcast. I find walking and listening helped me think more clearly. Sometimes you need a fresh perspective!

Use Case: Leading a workshop at school

Let’s say you’re leading a workshop on misinformation. You upload a selection of real news articles, policy briefings, and social media screenshots. Participants then ask NotebookLM questions like:

“What’s the key argument in each source?”
“Which claims are supported by evidence?”
“Are there contradictions between sources?”

Now your learners are doing something powerful. They’re not just reading passively; they’re engaging in source interrogation. And with NotebookLM, they can do this independently or in pairs, freeing you up to facilitate deeper reflection.

This is how we build AI literacy + critical thinking – by teaching that AI is a lens, not a truth machine.

What about copyright, privacy, and ethical use?

NotebookLM does not share your content with other users, and your uploads are not added to Google’s training data. However, it is important to remember that NotebookLM remains a Google product. Institutional users should exercise caution and check local policies before introducing it widely.

Keep these guidelines in mind:

Use public domain, Creative Commons, OER, or institutionally licensed materials

Avoid uploading sensitive or personally identifiable information

If unsure, check with your compliance team, copyright librarian, or licensing officer

The platform also supports multilingual content, allowing users to ask questions in different languages. This can be particularly valuable in bilingual or international learning environments.

Final thoughts: Don’t automate understanding - augment it

NotebookLM is not a shortcut to knowledge, but a way of working with it more intelligently. For librarians and educators, it’s an opportunity to reclaim our role as guides – using AI to deepen engagement, not bypass it. The best way to understand its potential is to try it: start with a course syllabus, a policy paper, or your conference notes. See how it organises the material, then explore the suggested questions. This is our domain so let’s claim it, shape it, and share it.

October 3, 2025

Clare Bilobrk

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Frequently Asked Questions (FAQs):

1. What makes NotebookLM different from other AI tools like ChatGPT or Claude?

NotebookLM is “source-grounded.” It only works with the documents you upload, rather than drawing on training data or the open internet. This keeps responses tied directly to your chosen sources, reducing the risk of hallucinations or irrelevant answers.

2. Can NotebookLM save time for busy librarians and educators?

Yes. By consolidating multiple sources, documents, articles, transcripts, videos, podcasts and more, into a single notebook, it lets you ask questions, extract summaries, and generate study guides without toggling between dozens of tabs.

3. Is NotebookLM free to use?

Currently, NotebookLM is free in its beta version. Google may develop premium features in the future, but most library and education professionals will find the free version sufficient for experimentation.

4. How secure is NotebookLM for institutional use?

Your uploads remain private and are not added to Google’s training data. However, NotebookLM is still a Google product, so institutions should check compliance and copyright policies before uploading sensitive or licensed content.

5. What are some practical use cases for NotebookLM in libraries?

Creating study packs or revision guides from course materials
Preparing conference papers and workshop materials
Teaching students to interrogate multiple sources for consistency
Demonstrating summarisation, note-taking, and evidence extraction

6. What are NotebookLM’s current limitations?

It won’t pull in live web data, can’t always handle poorly formatted documents, and may require users to refine prompts for best results. Its honesty – admitting when an answer isn’t in your sources – is a limitation that doubles as a valuable teaching feature.

Tagged AI tools, librarians, NotebookLM, research